From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Mon, 4 Sep 2017 18:44:23 +0100 Subject: COMPMID-344 Updated doxygen Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae --- .../NEON/functions/NEFullyConnectedLayer.cpp | 344 +++++++++++++++++++++ 1 file changed, 344 insertions(+) create mode 100644 src/runtime/NEON/functions/NEFullyConnectedLayer.cpp (limited to 'src/runtime/NEON/functions/NEFullyConnectedLayer.cpp') diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp new file mode 100644 index 0000000000..abb41e9f70 --- /dev/null +++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp @@ -0,0 +1,344 @@ +/* + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h" + +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" + +#include +#include + +using namespace arm_compute; + +NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights() + : _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false) +{ +} + +void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON(output == nullptr); + ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() != 2); + ARM_COMPUTE_ERROR_ON((transpose_weights == false) && (is_batched_fc_layer == false)); + + const DataType dt = input->info()->data_type(); + const int fixed_point_position = input->info()->fixed_point_position(); + + _transpose_weights = transpose_weights; + _is_batched_fc_layer = is_batched_fc_layer; + + // Check if we need to transpose the weights + if(_transpose_weights) + { + if(_is_batched_fc_layer) + { + // Initialize the output tensor for transpose + TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0)); + _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, dt, fixed_point_position)); + _transpose_kernel.configure(input, &_transpose_output); + + // Configure transpose 1xW kernel + _transpose1xW_kernel.configure(&_transpose_output, output); + + // Allocate temporary tensor used for transposing the weights + _transpose_output.allocator()->allocate(); + } + else + { + _transpose_kernel.configure(input, output); + } + } + else + { + if(_is_batched_fc_layer) + { + // Configure transpose 1xW kernel + _transpose1xW_kernel.configure(input, output); + } + else + { + ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported"); + } + } +} + +void NEFullyConnectedLayerReshapeWeights::run() +{ + if(_transpose_weights) + { + NEScheduler::get().schedule(&_transpose_kernel, Window::DimY); + } + if(_is_batched_fc_layer) + { + NEScheduler::get().schedule(&_transpose1xW_kernel, Window::DimY); + } +} + +NEFullyConnectedLayer::NEFullyConnectedLayer() + : _im2col_kernel(), _reshape_weights_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _reshape_weights_output(), + _are_weights_reshaped(false), _is_fc_after_conv(false), _is_batched_fc_layer(false), _accumulate_biases(false) +{ +} + +void NEFullyConnectedLayer::configure_conv_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output) +{ + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2) * (16 / weights->info()->element_size()))); + + const DataType dt = input->info()->data_type(); + const int fixed_point_position = input->info()->fixed_point_position(); + + // If the fully connected layer is called after a convolution layer, the input tensor must be linearized + + // Initialize output tensor for im2col + TensorShape shape_im2col; + shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)); + shape_im2col.set(1, input->info()->dimension(3)); + shape_im2col.set(2, input->info()->dimension(4)); + shape_im2col.set(3, input->info()->dimension(5)); + _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); + + // Initialize output tensor for interleave 4x4 + TensorShape shape_interleaved = _im2col_output.info()->tensor_shape(); + shape_interleaved.set(0, shape_interleaved.x() * 4); + shape_interleaved.set(1, std::ceil(static_cast(shape_interleaved.y()) / 4)); + _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); + + // Configure im2col kernel + _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false); + + // Configure interleave4x4 kernel + _interleave4x4_kernel.configure(&_im2col_output, &_interleave4x4_output); + + // Configure matrix multiply kernel + _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f); + + // Allocate the tensors once all the configure methods have been called + _im2col_output.allocator()->allocate(); + _interleave4x4_output.allocator()->allocate(); +} + +void NEFullyConnectedLayer::configure_fc_fc_wb(const ITensor *input, const ITensor *weights, ITensor *output) +{ + const DataType dt = input->info()->data_type(); + const int fixed_point_position = input->info()->fixed_point_position(); + + // Initialize output tensor for interleave 4x4 + TensorShape shape_interleaved = input->info()->tensor_shape(); + shape_interleaved.set(0, shape_interleaved.x() * 4); + shape_interleaved.set(1, std::ceil(static_cast(shape_interleaved.y()) / 4)); + _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); + + // Configure interleave4x4 kernel + _interleave4x4_kernel.configure(input, &_interleave4x4_output); + + // Configure matrix multiply kernel + _mm_kernel.configure(&_interleave4x4_output, weights, output, 1.0f); + + // Allocate the tensors once all the configure methods have been called + _interleave4x4_output.allocator()->allocate(); +} + +void NEFullyConnectedLayer::configure_conv_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output) +{ + ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)))); + + const DataType dt = input->info()->data_type(); + const int fixed_point_position = input->info()->fixed_point_position(); + + // If the fully connected layer is called after a convolution layer, the input tensor must be linearized + + // Initialize output tensor for im2col + TensorShape shape_im2col; + shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)); + shape_im2col.set(1, 1); + _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); + + // Configure im2col kernel + _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false); + + // Configure matrix multiply kernel + _mm_kernel.configure(&_im2col_output, weights, output, 1.0f); + + // Allocate the output tensor for im2col once all the configure methods have been called + _im2col_output.allocator()->allocate(); +} + +void NEFullyConnectedLayer::configure_fc_fc_nb(const ITensor *input, const ITensor *weights, ITensor *output) +{ + ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1)); + + // Configure matrix multiply kernel + _mm_kernel.configure(input, weights, output, 1.0f); +} + +void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights, bool are_weights_reshaped) +{ + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); + ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2); + + const DataType dt = input->info()->data_type(); + const int fixed_point_position = input->info()->fixed_point_position(); + + _are_weights_reshaped = are_weights_reshaped; + _is_fc_after_conv = true; + _is_batched_fc_layer = false; + _accumulate_biases = false; + + if(biases != nullptr) + { + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + + _accumulate_biases = true; + + // Configure accumulate biases kernel + _accumulate_biases_kernel.configure(output, biases); + } + + // With the Fully Connected layer we can have 4 different cases: + // 1) Convolution layer -> Fully Connected layer without batches + // 2) Fully Connected layer -> Fully Connected layer without batches + // 3) Convolution layer -> Fully Connected layer with batches + // 4) Fully Connected layer -> Fully Connected layer with batches + + // Check if we have a fully connected layer with batches + _is_batched_fc_layer = (output->info()->dimension(1) > 1); + + const ITensor *weights_to_use = weights; + + if(!are_weights_reshaped) + { + if((transpose_weights || _is_batched_fc_layer)) + { + weights_to_use = &_reshape_weights_output; + + if(transpose_weights) + { + if(_is_batched_fc_layer) + { + const float transpose_width = 16.0f / input->info()->element_size(); + TensorShape shape_wt(weights->info()->dimension(0) * static_cast(transpose_width), static_cast(std::ceil(weights->info()->dimension(1) / transpose_width))); + TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); + _reshape_weights_output.allocator()->init(info_wt); + } + else + { + TensorShape shape_wt(weights->info()->dimension(1), weights->info()->dimension(0)); + TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); + _reshape_weights_output.allocator()->init(info_wt); + } + } + else + { + ARM_COMPUTE_ERROR_ON(!_is_batched_fc_layer); + + const float transpose_width = 16.0f / input->info()->element_size(); + TensorShape shape_wt(weights->info()->dimension(1) * static_cast(transpose_width), static_cast(std::ceil(weights->info()->dimension(0) / transpose_width))); + TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); + _reshape_weights_output.allocator()->init(info_wt); + } + + // Reshape the weights + _reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer); + } + } + + if(_is_batched_fc_layer) + { + _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3, + input->info()->tensor_shape().cend(), + output->info()->tensor_shape().cbegin() + 1)); + + if(_is_fc_after_conv) + { + // Fully Connected layer after a Convolution Layer with batches + configure_conv_fc_wb(input, weights_to_use, output); + } + else + { + // Fully Connected layer after a Fully Connected Layer with batches + configure_fc_fc_wb(input, weights_to_use, output); + } + } + else + { + // In case of not batched fully connected layer, the weights will not be reshaped using transposed1xW + _is_fc_after_conv = ((weights_to_use->info()->dimension(1)) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))); + + if(_is_fc_after_conv) + { + // Fully Connected layer after a Convolution Layer without batches + configure_conv_fc_nb(input, weights_to_use, output); + } + else + { + // Fully Connected layer after a Fully Connected Layer without batches + configure_fc_fc_nb(input, weights_to_use, output); + } + } + + // Allocate the transpose tensor if the are_weights_reshaped flag is false and once all the configure methods have been called + if(!are_weights_reshaped) + { + if(transpose_weights || _is_batched_fc_layer) + { + // Allocate the tensor for the weights reshaped + _reshape_weights_output.allocator()->allocate(); + } + } +} + +void NEFullyConnectedLayer::run() +{ + // Reshape of the weights (happens only once) + if(!_are_weights_reshaped) + { + _are_weights_reshaped = true; + _reshape_weights_kernel.run(); + } + + // Linearize input if comes from a convolutional layer + if(_is_fc_after_conv) + { + NEScheduler::get().schedule(&_im2col_kernel, Window::DimY); + } + + // Interleave input + if(_is_batched_fc_layer) + { + NEScheduler::get().schedule(&_interleave4x4_kernel, Window::DimY); + } + + // Run matrix multiply + NEScheduler::get().schedule(&_mm_kernel, _is_batched_fc_layer ? Window::DimY : Window::DimX); + + // Accumulate biases if provided + if(_accumulate_biases) + { + NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY); + } +} -- cgit v1.2.1